Abstract

Electronic products enter the waste stream rapidly due to technological enhancements. Their parts and material recovery involve significant economic and environmental gain. To regain the value added to such products a certain level of disassembly may be required. Disassembly operations are often expensive and the complexity of determining the best disassembly sequence increases as the number of parts in a product grows. Therefore, it is necessary to develop methodologies for obtaining optimal or near optimal disassembly sequences to ensure efficient recovery process. To that end, this chapter introduces a Genetic Algorithm based methodology to develop disassembly sequencing for end-of-life products. A numerical example is presented to provide and demonstrate better understating and functionality of the algorithm.

The robotic manipulator includes a waist motor to affect rotations about the Z-axis. In the X-Y plane; a shoulder, elbow and wrist joints enable navigation in 3D. The arm is connected to a hand griper that has the capability of rotation about the X-axis for the handling of parts. The top speed of the arm is 1,000 m/s according to Mitsubishi co. (n.d.), thus enabling the applications within the mass production arena; including computers disassembly which is the main focus of this chapter.

As mentioned before, EOL products disassembly is one of new applications that can be used to reduce waste and improve recycling processes. The main design challenges in this process include the assessment of the usefulness of parts to be disassembled and the reduction of the utilized time. A robotic manipulator can be utilized to save time and improve the performance of the process. One of the main issues in using the robotic arm in disassembly is process planning, through which we need to compute the path required to remove items in the EOL product with minimum waste. This can be achieved by feeding an optimum disassembly sequence into the robotic arm. The optimal sequence is the path that would generate the maximum gain while minimizing the overall disassembly time. In this chapter, we implement Genetic Algorithm (GA) in determining the optimal/near optimal disassembly sequence.